Analyzation of Patient-Authored Text Data and Extract Depression Symptoms using Lexical Analysis
Keywords:
Internet-Delivered Interventions, NLP, Tailored Intervention, Personalization, Adaptive Treatments, Adaptive Strategies, Adaptive iCBTAbstract
Background: Internet-Delivered Psychological Treatment (IDPT) systems have the potential to provide evidence-based mental health treatments for a far-reaching population at a lower cost. However, most of the current IDPT systems follow a tunnel based treatment process and do not adapt to the needs of different patients’. In this paper, we explore the possibility of applying Natural Language Processing (NLP) for personalizing mental health interventions. Objective: The primary objective of this study is to present an adaptive strategy based on NLP techniques that analyses patient-authored text data and extract depression symptoms based on a clinically established assessment questionnaire, PHQ-9. Method: We propose a novel word-embedding (Depression2Vec) to extract depression symptoms from patient authored text data and compare it with three state-of-the-art NLP techniques. We also present an adaptive IDPT system that personalizes treatments for mental health patients based on the proposed depression symptoms detection technique. Result: Our results indicate that the performance of proposed embedding Depression2Vec is comparable to WordNet, but in some cases, the former outperforms the latter with respect to extracting depression symptoms from the patient-authored text. Conclusion: Although extraction of symptoms from text is challenging, our proposed method can effectively extract depression symptoms from text data, which can be used to deliver the personalized intervention.
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References
A. e. a. Konrad, “Finding the adaptive sweet spot: Balancing compliance and achievement in automated stress reduction,” in Conference on Human Factors in Computing Systems - Proceedings, vol. 2015-April, (New York, New York, USA), pp. 3829–3838, Association for Computing Machinery, 4 2015.
S. K. Mukhiya, F. Rabbi, K. I. Pun, and Y. Lamo, “An architectural design for self-reporting e-health systems,” in Proceedings – 2019 IEEE/ACM 1st International Workshop on Software Engineering for Healthcare, SEH 2019, pp. 1–8, Institute of Electrical and Electronics Engineers Inc., 5 2019.
D. e. a. Cer, “Universal Sentence Encoder,” EMNLP 2018 – Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings, pp. 169–174, 3 2018.
J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global vectors for word representation,” in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2014.
G. Miller, C. Fellbaum, J. Kegl, and K. Miller, “WordNet: An Electronic Lexical Reference System Based on Theories of Lexical Memory,” Revue qu´eb´ecoise de linguistique, vol. 17, pp. 181–212, 5 2009.
B. e. a. Funk, “A Framework for Applying Natural Language Processing in Digital Health Interventions.,” Journal of medical Internet research, vol. 22, p. e13855, 2 2020.
A. H. Yazdavar, H. S. Al-Olimat, M. Ebrahimi, G. Bajaj, T. Banerjee, K. Thirunarayan, J. Pathak, and A. Sheth, “Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media,” in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, 2017.
D. Andrzejewski and X. Zhu, “Latent Dirichlet Allocation with Topicin- Set Knowledge *,” 2009.
D. Ramage, C. D. Manning, and S. Dumais, Partially Labeled Topic Models for Interpretable Text Mining. 2011.
C. Karmen, R. C. Hsiung, and T. Wetter, “Screening internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods,” Computer Methods and Programs in Biomedicine, vol. 120, pp. 27–36, 6 2015.
K. Kroenke, R. L. Spitzer, and J. B. Williams, “The PHQ-9: Validity of a brief depression severity measure,” Journal of General Internal Medicine, vol. 16, no. 9, pp. 606–613, 2001.
N. e. a. Americans, “The ICD-10 Classification of Mental and Behavioural Disorders,” IACAPAP e-Textbook of child and adolescent Mental health, vol. 55, no. 1993, pp. 135–139, 2013.
X. Liu, J. Meehan, W. Tong, L. Wu, X. Xu, and J. Xu, “DLI-IT: A deep learning approach to drug label identification through image and text embedding,” BMC Medical Informatics and Decision Making, 2020.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, International Conference on Learning Representations, ICLR, 2013.
W. G. Charles, “Contextual correlates of meaning,” Applied Psycholinguistics, vol. 21, no. 4, pp. 505–524, 2000.
S. K. Mukhiya, J. Wake, Y. Inal, and Y. Lamo, “Adaptive Systems for Internet-Delivered Psychological Treatments (In review).,” IEEE Access, 2020.
B. M. Bewick, K. Trusler, B. Mulhern, M. Barkham, and A. J. Hill, “The feasibility and effectiveness of a web-based personalised feedback and social norms alcohol intervention in UK university students: A randomised control trial,” Addictive Behaviors, vol. 33, pp. 1192–1198, 9 2008.
Z. Hilvert-Bruce, P. J. Rossouw, N. Wong, M. Sunderland, and G. Andrews, “Adherence as a determinant of effectiveness of internet cognitive behavioural therapy for anxiety and depressive disorders,” Behaviour Research and Therapy, vol. 50, pp. 463–468, 8 2012.
C. Dreisbach, T. A. Koleck, P. E. Bourne, and S. Bakken, “A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data,” 5 2019.
S. Perera, A. Sheth, K. Thirunarayan, S. Nair, and N. Shah, “Challenges in understanding clinical notes: Why NLP engines fall short and where background knowledge can help,” in International Conference on Information and Knowledge Management, Proceedings, (New York, New York, USA), pp. 21–26, ACM Press, 2013.
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